Machine-learning based high-bandwidth magnetic sensing by Galya Haim, Stefano Martina, John Howell, Nir Bar-Gill, Filippo Caruso

“…Our results indicate a potential reduction of required data points by at least a factor of 3, while maintaining the current error level. …”
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Semantic SLAM using laser-vision data fusion: Enhancing autonomous navigation in unstructured environments by Ning Chen, Dong Wei, Dongsheng Lin, Linhan Lin

“…Additionally, the size error between the generated map and the actual scene was reduced to just 0.81 %, indicating high fidelity in mapping accuracy. …”
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An ensemble-driven machine learning framework for enhanced water quality classification by Preet Singh, Taniya Hasija, Salil Bharany, Hafiza Nazra Tun Naeem, B. Chinna Rao, Seada Hussen, Ateeq Ur Rehman

“…The soft voting ensemble model, therefore, shows a relative improvement of 1.46% in accuracy over the best-performing base learner and a 27.8% reduction in its error rate. It is, therefore, confirmed that ensemble learning soft voting improves the reliability of water quality classification as well as accuracy, thus providing a strong platform for future environmental monitoring systems.…”
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Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule by Liu Zhang, Bo Xing, Yanbing Gao, Lei Yao, Dengfeng Zhao, Jinquan Ding, Yanyan Li

“…The findings reveal a relative error of approximately 4% when maintaining lithium-ion battery SOH at 80%, affirming the GPR model’s high accuracy and robust adaptability for SOH prediction.…”
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SVR-Optimized ANN Model for Predicting Earthquake Risk in Electrical Substations Based on Disaster Datasets in the Aceh Region, Indonesia by Elvy Sahnur Nasution, Yuwaldi Away, Syahrial, Ira Devi Sara, Andri Novandri

“…This improvement is indicated by a reduction in error and an increase in the coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>). …”
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Financial Policies and Corporate Income Tax Administration in Nigeria by Cordelia Onyinyechi Omodero, Joy Limaro Yado

“…This investigation applies autoregressive distributed lag and error correction models, acknowledging the existence of a long-term relationship within the series. …”
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Improving Georeferencing Accuracy in Drone Imagery: Combining Drone Camera Angles with High and Variable Fields of View by Vishal Nagpal, Manoj Devare

“…The proposed approach further shows a quantitative improvement of 12.50% to 75.0% in the geolocation error reduction claimed. It was achieved by the decrease of MAE from 0.108 km to 0.055 km while RMSE was lowered from 0.111 km to 0.057 km indicating the reliability of the method. …”
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Experimental investigation and optimization of epoxy composites reinforced with jute fiber and alumina using the Jaya ANFIS approach by Lakshmi Narayana Somsole, P. Thejasree, K. L. Narasimhamu, Manikandan Natarajan, G. Velmurugan, Dinesh Ramesh Salunke, Vinod P. Sakhare, Pramod Kumar, Regasa Yadeta Sembeta

“…Abstract Composites reinforced with natural fibers are increasingly progressively in diverse engineering practices for their remarkable attributes, including weight reduction, high strength, cost-efficiency, biodegradability, and renewability. …”
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Enhancing weather index insurance through surrogate models: leveraging machine learning techniques and remote sensing data by Sachini Wijesena, Biswajeet Pradhan

“…The GLM achieved a mean absolute error (MAE) of 8.2%, which is comparable to the neural network model’s MAE of 7.6%. …”
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Determination of cervical vertebral maturation using machine learning in lateral cephalograms by Shahab Kavousinejad, Asghar Ebadifar, Azita Tehranchi, Farzan Zakermashhadi, Kazem Dalaie

“…A ratio-based approach was employed to compute the values of C3 and C4, accompanied by the implementation of an auto_error_reduction (AER) function to enhance the accuracy of landmark selection. …”
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Accelerated Modeling of Transients in Electromagnetic Devices Based on Magnetoelectric Substitution Circuits by Sergii Tykhovod, Ihor Orlovskyi

“…The simulation of processes over a long time interval demonstrate error reduction and stabilization. This indicates the potential of the proposed method for simulating processes in more complex electromagnetic devices, (for example, transformers).…”
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The impact of study size on meta-analyses: examination of underpowered studies in Cochrane reviews. by Rebecca M Turner, Sheila M Bird, Julian P T Higgins

“…We defined adequate power as ≥50% power to detect a 30% relative risk reduction. In a subset of 1,107 meta-analyses including 5 or more studies with at least two adequately powered and at least one underpowered, results were compared with and without underpowered studies. …”
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Simulation Study on the Accuracy of Modified Distributed Beamforming in Phased Array Weather Radars for Various Precipitation Events by Steffy Benny, Swaroop Sahoo, S. Athira, V. Chandrasekar

“…The modified DB technique radar variables have been found to have error less than 5 dBZ for reflectivity, 1 dB for differential reflectivity and 0.08 for correlation coefficient. …”
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Data-driven intelligent productivity prediction model for horizontal fracture stimulation by Qian Li, Yiyong Sui, Mengying Luo, Bin Guan, Lu Liu, Yuan Zhao

“…Field validation showed that the productivity prediction model achieved an average error of 7.06%, providing a basis for horizontal fracture engineering design and achieving cost reduction and efficiency improvement in oilfield development.…”
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Tribological Performance of Electrochemically Textured EN-GJS 400-15 Spheroidal Cast Iron by Peng Jiang, Jonathon Mitchell-Smith, John Christopher Walker

“…Textured surfaces exhibited a more pronounced friction performance at 50 N than at 11 N, exhibiting a consistent friction reduction of up to 18.8% compared to the untextured surface. …”
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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…The optimized loss function further improves the prediction performance, resulting in a significant improvement in the accuracy of flood peak prediction, with a reduction of 0.26% in the relative error of the peak prediction by the GWN model. …”
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Real-Time TECS Gain Tuning Using Steepest Descent Method for Post-Transition Stability in Unmanned Tilt-Rotor eVTOLs by Choonghyun Lee, Ngoc Phi Nguyen, Sangjun Bae, Sung Kyung Hong

“…Simulation results demonstrate that the SD-TECS approach significantly improves control performance compared to the default PX4 TECS, achieving a 35.5% reduction in the altitude settling time, a 57.3% improvement in the airspeed settling time, and a 66.1% decrease in the integrated altitude error. …”
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Integrated top-down process and voxel-based microstructure modeling for Ti-6Al-4V in laser wire direct energy deposition process by Yousub Lee, Peeyush Nandwana, Brian Gibson, Paritosh Mhatre, Julio Ortega Rojas, Bhagyashree Prabhune, Aaron Thornton, Joshua Vaughan, Srdjan Simunovic

“…This approach anticipates a significant reduction in data points and computation time and resources. …”
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Geographically Aware Air Quality Prediction Through CNN-LSTM-KAN Hybrid Modeling with Climatic and Topographic Differentiation by Yue Hu, Yitong Ding, Wenjing Jiang

“…Comparative experiments demonstrated superior performance with a 23.6–59.6% reduction in Root-Mean-Square Error (RMSE) relative to baseline LSTM models, along with consistent outperformance over CNN-LSTM hybrids. …”
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Separating the albedo-reducing effect of different light-absorbing particles on snow using deep learning by L.-A. Chevrollier, A. Wehrlé, J. M. Cook, N. Pirk, L. G. Benning, A. M. Anesio, M. Tranter

“…The inversion method was applied to 180 ground field spectra collected on snowfields in southern Norway, with a mean absolute error on spectral albedo of 0.0056, and surface parameters that closely matched expectations from qualitative assessments of the surface. …”
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